def fisicoAmbiental(Bibliography, Entities):
df1 = nfv.dfFix(Bibliography, "Latitude", "Topography")
df2 = nfv.dfFix(Entities, "Water_table", "Sanitation:Personal_hygiene")
FA_geographicIdentification = nt.concatDF(df1, df2)
nt.mkCSV(FA_geographicIdentification, "FA_geographicIdentification.csv")
FA_Topography = nfv.dfFix(Bibliography, "Upper bound (m)", "FOOD SECURITY")
nt.mkCSV(FA_Topography, "FA_Topography.csv")
FA_NaturalResource = nfv.dfFix(Bibliography, "r.1",
"ACTORS (PARTNERS) IDENTIFICATION")
FA_NaturalResource = FA_NaturalResource.dropna(axis=1)
FA_NaturalResource = np.array(FA_NaturalResource)
bound = []
resource = []
for row in FA_NaturalResource:
for elem in row:
bound = np.append(bound, elem)
resource = np.append(resource, 'river')
bound = pd.DataFrame(bound)
bound = bound.reset_index(drop=True)
resource = pd.DataFrame(resource)
resource = resource.reset_index(drop=True)
FA_NaturalResource = nt.concatDF(bound, resource)
nt.mkCSV(FA_NaturalResource, "FA_NaturalResource.csv")
def get_applianceDF(df):
df1 = nfv.dfFix(df, 0, 1)
df2 = nfv.dfFix(df, 1)
array = np.array([])
appliance = np.array([])
for row in np.array(df1):
corpus = np.array([])
for elem in row:
corpus = np.append(corpus, [elem])
count_vectorizer = CountVectorizer(
stop_words=spanish_stopwords + english_stopwords,
vocabulary=[
'light_bulbs', 'mobile_phone', 'radio_music_pl', 'tv_dvd',
'laptop_tablet_', 'fridge', 'electrical_sto', 'others'
])
X = count_vectorizer.fit_transform(corpus)
array = count_vectorizer.get_feature_names()
for elem in array:
appliance = np.append(appliance, elem)
appliance = pd.DataFrame(appliance)
hours = np.array([])
for row in np.array(df2):
for elem in row:
hours = np.append(hours, elem)
hours = pd.DataFrame(hours)
hours = (hours.dropna())
return concatDF(appliance, hours)
def seDemoCult(Entities, HouseHold):
df1 = nfv.dfFix(Entities, "Population:Women:Infants",
"Population:Men:Infants_001")
df2 = nfv.dfFix(Entities, "Population:Men:Infants_001",
"Fuel_Cost:Main_Fuel")
df1 = np.array(df1)
df2 = np.array(df2)
array = np.array([], dtype=int)
array = df1 + df2
SE_population = pd.DataFrame(array)
nt.mkCSV(SE_population, "SE_population.csv")
SE_HouseHoldComposition = nfv.dfFix(HouseHold, "General:Old_women",
"Shelter:No_Rooms")
array = np.array(SE_HouseHoldComposition)
array[np.isnan(array)] = 0
array = array.astype(int)
young = array[:, 1] + array[:, 3]
array[:, 1] = array[:, 2]
array[:, 2] = young
array[:, 3] = array[:, 4]
array = np.delete(array, 4, 1)
SE_HouseHoldComposition = pd.DataFrame(array)
nt.mkCSV(SE_HouseHoldComposition, "SE_HouseHoldComposition.csv")
SE_PersonalHygiene = nfv.dfFix(Entities, "Sanitation:Personal_hygiene",
"Sanitation:Excreta")
nt.mkCSV(SE_PersonalHygiene, "SE_PersonalHygiene.csv")
SE_CleaningMaterial = nfv.dfFix(Entities, "Sanitation:Excreta",
"Sanitation:Open_defecation")
nt.mkCSV(SE_CleaningMaterial, "SE_CleaningMaterial.csv")
def corralCrop(LocalLeaders, FarmyardCrop):
df1 = nfv.dfFix(LocalLeaders, "Food_security:Grazing_technique",
"Costs:basic_basket")
df2 = nfv.dfFix(LocalLeaders, "Food_security:fertilizers",
"Food_security:storing_food")
df2 = df2.isin(["yes"])
FS_CorralCropData = nt.concatDF(df1, df2)
nt.mkCSV(FS_CorralCropData, "FS_CorralCropData.csv")
df1 = nfv.dfFix(FarmyardCrop, "Item", "Property")
df1 = df1.isin(["crop_area"])
df2 = nfv.dfFix(FarmyardCrop, "Record_your_current_location:Latitude",
"Record_your_current_location:Accuracy")
df3 = nfv.dfFix(FarmyardCrop, "Property", "Drainage")
df4 = nfv.dfFix(FarmyardCrop, "Drainage",
"Irrigation") #No se ajusta dato a Requisitos
df4 = df4.isin(["yes"])
FS_CorralUbication = nt.concatDF(df2, nt.concatDF(df3, df4))
FS_CorralUbication = nt.get_valueBySector(df1, FS_CorralUbication)
nt.mkCSV(FS_CorralUbication, "FS_CorralUbication.csv")
df1 = nfv.dfFix(FarmyardCrop, "Item", "Property")
df1 = df1.isin(["farmyard"])
df2 = nfv.dfFix(FarmyardCrop, "Record_your_current_location:Latitude",
"Record_your_current_location:Accuracy")
df3 = nfv.dfFix(FarmyardCrop, "Property", "Drainage")
df4 = nfv.dfFix(FarmyardCrop, "Irrigation",
"Irrigation_details:Water_pump")
df4 = df4.isin(["yes"]) #No se ajusta dato a Requisitos
FS_CropUbication = nt.concatDF(df2, nt.concatDF(df3, df4))
FS_CropUbication = nt.get_valueBySector(df1, FS_CropUbication)
nt.mkCSV(FS_CropUbication, "FS_CropUbication.csv")
def mobility(MobilityINF, GeneralForm):
#INF_MobilityInfrasctucture = nfv.dfFix(Entities,"","") #no se encuentra el dato en origen COMPROBADO 17/04/2020
INF_MobilityPoint = nfv.dfFix(MobilityINF,
"Record_your_current_location:Latitude",
"Record_your_current_location:Accuracy")
nt.mkCSV(INF_MobilityPoint, "INF_MobilityPoint.csv")
INF_MobilityWay = [
'walking', 'motorcycle', 'bike', 'truck_lorry_va', 'animals', 'car'
]
INF_MobilityWay = pd.DataFrame(INF_MobilityWay)
nt.mkCSV(INF_MobilityWay, "INF_MobilityWay.csv")
internal = nfv.dfFix(GeneralForm, "Transport:Kind_transport_inside",
"Transport:Kind_transport_outside")
internal = nt.separateValues(internal)
internal = nt.set_sector(internal, "internal")
external = nfv.dfFix(GeneralForm, "Transport:Kind_transport_outside",
"meta:instanceID")
external = nt.separateValues(external)
external = nt.set_sector(external, "external")
INF_MobilityWay_has_Community = nt.concatDF(internal.T, external.T).T
nt.mkCSV(INF_MobilityWay_has_Community,
"INF_MobilityWay_has_Community.csv")
def government(Bibliography):
#G_PublicPolitic no forma parte ETL
dfPublic = nfv.dfFix(Bibliography,"Public institutions","Private institutions")
dfPrivate = nfv.dfFix(Bibliography,"Private institutions","Non-profit organizations/NGOs")
dfNonProfit = nfv.dfFix(Bibliography,"Non-profit organizations/NGOs","International cooperation agencies")
dfInternational = nfv.dfFix(Bibliography,"International cooperation agencies","Local representatives/local committees/ local liders")
dfLocal = nfv.dfFix(Bibliography,"Local representatives/local committees/ local liders")
politicalActor = nt.politicalActor(dfPublic,dfPrivate,dfNonProfit,dfInternational,dfLocal)
nt.mkCSV(politicalActor,"g_politicalActor.csv")
def camp(Bibliography, Entities):
df1 = nfv.dfFix(Bibliography,
"Implementation date of the refugee camp (year)",
"Migration reasons")
df2 = nfv.dfFix(Entities, "GENERAL_INFORMATION:Secondary_movement",
"GENERAL_INFORMATION:Relationship")
df2 = df2['GENERAL_INFORMATION:Secondary_movement'].mean()
array = np.array([df2])
camp = nt.concatDF(df1, pd.DataFrame(array))
nt.mkCSV(camp, "camp.csv")
def sanitation(Entities, SanitationInf):
df1 = nfv.dfFix(Entities, "Sanitation:Open_defecation",
"Sanitation:Type_of_Latrine")
df1 = df1.isin(["yes"])
df2 = nfv.dfFix(Entities, "Sanitation:Type_of_Latrine",
"Sanitation:Individual_Latrines")
INF_SanitationAccess = nt.concatDF(df1, df2)
nt.mkCSV(INF_SanitationAccess, "INF_SanitationAccess.csv")
inf_sanitationsystemquality = nfv.dfFix(SanitationInf, "Slab",
"meta:instanceID")
inf_sanitationsystemquality = inf_sanitationsystemquality.isin(["yes"])
nt.mkCSV(inf_sanitationsystemquality, "inf_sanitationsystemquality.csv")
def source(GeneralCitizen,LocalLeaders):
FS_FoodSource = ["Humanitarian Aid","Crops","Market"]
FS_FoodSource = pd.DataFrame(FS_FoodSource)
nt.mkCSV(FS_FoodSource,"FS_FoodSource.csv")
df1 = nfv.dfFix(GeneralCitizen,"Main_food_source:Humanitarian_Aid","meta:instanceID")
source = pd.DataFrame(["Humanitarian Aid","Crops","Market"])
FS_FoodSource_has_Community = nt.get_number(df1,source)
nt.mkCSV(FS_FoodSource_has_Community,"FS_FoodSource_has_Community.csv")
FS_CultivationSeason = nfv.dfFix(LocalLeaders,"Food_security:cultivation_months","Food_security:own_food_months")
FS_CultivationSeason = nt.vectorizeValue(FS_CultivationSeason)
nt.mkCSV(FS_CultivationSeason,"FS_CultivationSeason.csv")
def shelter(Entities, Shelter, HouseHold):
SH_Shelter = nfv.dfFix(Entities, "Shelter:Total_shelter",
"Shelter:Vulnerable_Area:Vunerable_Area")
nt.mkCSV(SH_Shelter, "SH_Shelter.csv")
df1 = nfv.dfFix(Shelter, "Location:Latitude", "Location:Accuracy")
df2 = nfv.dfFix(Shelter, "Construc_tion_Details:Appropiate_Roof",
"Construc_tion_Details:Picture_Outside")
df2 = df2.isin(["yes"])
SH_Building = nt.concatDF(df1, df2)
nt.mkCSV(SH_Building, "SH_Building.csv")
SH_House = nfv.dfFix(HouseHold, "Shelter:No_Rooms",
"Energy:Access_Y_N_001")
nt.mkCSV(SH_House, "SH_House.csv")
def get_tablePK(table, cursor):
cursor.execute("SELECT * FROM " + table)
df1 = uniFormatTable(pd.DataFrame(cursor.fetchall()))
df2 = pd.read_csv(nfv.getPath(finalpath, table + ".csv"),
float_precision="high")
df2 = uniFormatDF(df2)
df1 = np.array(df1)
pk = np.array([])
for index, row in df2.iterrows():
if (serviceTable(table) == False):
for row2 in df1:
if ((np.equal(np.array(row), np.array(row2[1:]))).all()):
pk = np.append(pk, row2[0])
else:
if (isEducationalCenter(table)):
for row2 in df1:
if ((np.equal(np.array(row[:-2]), row2[1:-3])).all()):
pk = np.append(pk, row2[0])
else:
if (isOtherCenter(table) == False):
for row2 in df1:
if ((np.equal(np.array(row[:-1]), row2[1:-2])).all()):
pk = np.append(pk, row2[0])
else:
for row2 in df1:
if ((np.equal(np.array(row[:-1]), row2[1:-1])).all()):
pk = np.append(pk, row2[0])
return pd.DataFrame(pk)
def wasteManagement(Entities, WasteManagementInf):
INF_WasteManagementgInfrastructure = nfv.dfFix(
Entities, "Waste_Managment:Waste_Collection",
"Waste_Managment:Landfill_Details:Location_1")
nt.mkCSV(INF_WasteManagementgInfrastructure,
"INF_WasteManagementgInfrastructure.csv")
INF_landFill = nfv.dfFix(Entities,
"Waste_Managment:Landfill_Details:Location_1",
"Water:Quality")
nt.mkCSV(INF_landFill, "INF_landFill.csv")
INF_CollectionPoints = nfv.dfFix(WasteManagementInf,
"Record_your_current_location:Latitude",
"Record_your_current_location:Accuracy")
nt.mkCSV(INF_CollectionPoints, "INF_CollectionPoints.csv")
def continuity(LocalLeaders, ComunalServices):
FS_FoodAccessContinuity = nfv.dfFix(LocalLeaders,
"Food_security:perishable_food",
"Costs:basic_basket")
nt.mkCSV(FS_FoodAccessContinuity, "FS_FoodAccessContinuity.csv")
FS_SelfSufficiencySeason = nfv.dfFix(LocalLeaders,
"Food_security:own_food_months",
"Food_security:kind_food")
FS_SelfSufficiencySeason = nt.vectorizeValue(FS_SelfSufficiencySeason)
nt.mkCSV(FS_SelfSufficiencySeason, "FS_SelfSufficiencySeason.csv")
FS_OwnCultivationFoodType = nfv.dfFix(LocalLeaders,
"Food_security:kind_food",
"Food_security:fertilizers")
FS_OwnCultivationFoodType = nt.separateValues(FS_OwnCultivationFoodType)
nt.mkCSV(FS_OwnCultivationFoodType, "FS_OwnCultivationFoodType.csv")
FS_GrainConservation = nfv.dfFix(LocalLeaders, "Food_security:dry_food",
"Food_security:perishable_food")
FS_GrainConservation = nt.separateValues(FS_GrainConservation)
nt.mkCSV(FS_GrainConservation, "FS_GrainConservation.csv")
df1 = nfv.dfFix(ComunalServices, "General_Information:Type_of_service",
"General_Information:Other_service")
df1 = df1.isin(["gran_mill"])
df2 = nfv.dfFix(ComunalServices, "Grain_Mill_Details:Available",
"Grain_Mill_Details:Type")
df3 = nfv.dfFix(ComunalServices, "Grain_Mill_Details:Engine",
"Cementary_Details:Drainage")
FS_GrainMill = nt.concatDF(df2, df3)
FS_GrainMill = nt.get_valueBySector(df1, FS_GrainMill)
nt.mkCSV(FS_GrainMill, "FS_GrainMill.csv")
def serv(HouseHold, Entities):
#S_HealthCenterService #información de plano
S_MedicineAccess = nfv.dfFix(HouseHold, "health_001:Healthcare",
"Economy:FamilyHead")
S_MedicineAccess = S_MedicineAccess.isin(["yes"])
nt.mkCSV(S_MedicineAccess, "S_MedicineAccess.csv")
S_DataAccess = nfv.dfFix(Entities, "Data_Access", "Antenna")
S_DataAccess = nt.separateValues(S_DataAccess)
nt.mkCSV(S_DataAccess, "S_DataAccess.csv")
S_RepeaterAntena = nfv.dfFix(Entities, "Antenna", "meta:instanceID")
nt.mkCSV(S_RepeaterAntena, "S_RepeaterAntena.csv")
S_NoEducationCause = nfv.dfFix(Entities, "Education_Issues", "Data_Access")
S_NoEducationCause = nt.separateValues(S_NoEducationCause)
nt.mkCSV(S_NoEducationCause, "S_NoEducationCause.csv")
def makeNMtable(elem, cursor):
x = nm.getTableName(elem)
if (nm.is_non_zero_file(nfv.getPath(finalpath, x + ".csv"))):
if (nm.specialTable(elem) == False):
tablePK = nm.get_tablePK(x, cursor)
communityPK = nm.get_communityPK(elem, cursor)
arrayCommunity = np.array([])
for index, row in tablePK.iterrows():
arrayCommunity = np.append(arrayCommunity, communityPK[0][0])
nmTableFK = nt.concatDF(tablePK, pd.DataFrame(arrayCommunity))
if (os.path.isfile(finalpath + "/" + elem + ".csv")):
if (nm.is_non_zero_file(nfv.getPath(finalpath,
elem + ".csv"))):
df = np.array(pd.read_csv(finalpath + "/" + elem + ".csv"))
df = pd.DataFrame(df)
nmTableFK = nt.concatDF(nmTableFK, df)
else:
nmTableFK = nm.get_specialTable(x, elem, cursor)
nt.mkCSV(nmTableFK, elem + ".csv")
def food(Bibliography, GeneralCitizen):
FS_Cause = nfv.dfFix(Bibliography, "Cause 1",
"Affected groups due to food insecurity")
FS_Cause = FS_Cause.transpose()
FS_Cause = FS_Cause.dropna(axis=1)
nt.mkCSV(FS_Cause,
"FS_Cause.csv") #Incompleto falta separar por comunidadades
df1 = nfv.dfFix(Bibliography, "Children", "Calories of the typical dish")
df1 = df1.isin(["yes"])
df2 = nfv.dfFix(Bibliography, "Intake (g) - default value 70g-",
"GENERAL INFORMATION OF REFUGEES SETTLEMENT")
FS_FoodSafety = nt.concatDF(df1, df2)
nt.mkCSV(FS_FoodSafety,
"FS_FoodSafety.csv") #Incompleto falta separar por comunidadades
FS_FoodAccess = ["meat", "grain", "vegetable", "fruit"]
FS_FoodAccess = pd.DataFrame(FS_FoodAccess)
nt.mkCSV(FS_FoodAccess, "FS_FoodAccess.csv")
FS_FoodAccess_has_Community = nfv.dfFix(GeneralCitizen, "Type_Food:Meat",
"times:One_time")
FS_FoodAccess_has_Community = FS_FoodAccess_has_Community.transpose()
nt.mkCSV(FS_FoodAccess_has_Community, "FS_FoodAccess_has_Community.csv")
FS_TimesPerDay = ["one", "two", "three", "Greater than three"]
FS_TimesPerDay = pd.DataFrame(FS_TimesPerDay)
nt.mkCSV(FS_TimesPerDay, "FS_TimesPerDay.csv")
df1 = nfv.dfFix(GeneralCitizen, "times:One_time", "main_food:Breakfast")
times = pd.DataFrame([["one", "two", "three", "Greater than three"]])
FS_TimesPerDay_has_Community = nt.get_number(df1, times)
nt.mkCSV(FS_TimesPerDay_has_Community, "FS_TimesPerDay_has_Community.csv")
FS_ImportantMeal = ["Breakfast", "lunch", "coffe time", "dinner"]
FS_ImportantMeal = pd.DataFrame(FS_ImportantMeal)
nt.mkCSV(FS_ImportantMeal, "FS_ImportantMeal.csv")
df1 = nfv.dfFix(GeneralCitizen, "main_food:Breakfast", "typical_dish:Pork")
meal = pd.DataFrame(["Breakfast", "lunch", "coffe time", "dinner"])
FS_ImportantMeal_has_Community = nt.get_number(df1, meal)
nt.mkCSV(FS_ImportantMeal_has_Community,
"FS_ImportantMeal_has_Community.csv")
FS_TypicalPlate = [
"pork", "beef", "chicken", "lamp", "cereals", "legumes", "fruits"
]
FS_TypicalPlate = pd.DataFrame(FS_TypicalPlate)
nt.mkCSV(FS_TypicalPlate, "FS_TypicalPlate.csv")
df1 = nfv.dfFix(Bibliography, "Pork (200 kcal/100g)",
"Intake (g) - default value 70g-")
plate = pd.DataFrame(
["pork", "beef", "chicken", "lamp", "cereals", "legumes", "fruits"])
FS_TypicalPlate_has_Community = nt.get_number(df1, plate)
nt.mkCSV(FS_TypicalPlate_has_Community,
"FS_TypicalPlate_has_Community.csv")
def get_valueBySector(df1, df2):
df2 = df2.reset_index()
array1 = np.array(df1)
i = 0
for row in array1:
for elem in row:
if (elem == False):
df2 = nfv.dropRow(df2, i)
i += 1
df2 = df2.set_index('index')
return df2
def seGenderData(GeneralCitizen):
#SE_GenderData = nfv.dfFix(Entities,"","")
#nt.mkCSV(SE_GenderData,"SE_GenderData.csv") No existe dicho dato en los formularios
SE_WorkType = ["Firewood Collection", "Cooking"]
SE_WorkType = pd.DataFrame(SE_WorkType)
nt.mkCSV(SE_WorkType, "SE_WorkType.csv")
df1 = nfv.dfFix(GeneralCitizen, "Firewood_collection:Childs",
"Cooking:Childs_001")
df2 = nfv.dfFix(GeneralCitizen, "Cooking:Childs_001",
"TICs_Knowledge:Phone_Call")
df1 = df1.transpose()
df2 = df2.transpose()
df1 = df1.reset_index(drop=True)
df2 = df2.reset_index(drop=True)
SE_WorkType_has_Community = nt.concatDF(df1, df2).T
SE_WorkType = ["Firewood Collection", "Cooking"]
SE_WorkType_has_Community = nt.concatDF(SE_WorkType_has_Community,
pd.DataFrame(SE_WorkType))
nt.mkCSV(SE_WorkType_has_Community, "SE_WorkType_has_Community.csv")
def sePersonalSafty(WomenGroup,LocalLeaders,SanitationInfra,Entities):
SE_SafetyPlace = nfv.dfFix(WomenGroup,"Feel_Safe:Street_morning","Feel_Safe:Firewood_collection_001")
SE_SafetyPlace = nt.getSubColumnNames(SE_SafetyPlace,10)
nt.mkCSV(SE_SafetyPlace,"SE_SafetyPlace.csv")
SE_SafetyPlace_has_Community = nfv.dfFix(WomenGroup,"Feel_Safe:Street_morning","Feel_Safe:Firewood_collection_001")
SE_SafetyPlace = pd.DataFrame()
SE_SafetyPlace = nt.getSubColumnNames(SE_SafetyPlace_has_Community,10)
array1 = np.array([])
for row in np.array(SE_SafetyPlace_has_Community):
for row2 in np.array(SE_SafetyPlace):
for elem2 in row2:
array1 = np.append(array1,elem2)
SE_SafetyPlace_has_Community = pd.DataFrame(np.array(SE_SafetyPlace_has_Community))
SE_SafetyPlace_has_Community = nt.concatDF(SE_SafetyPlace_has_Community.T,pd.DataFrame(array1))
nt.mkCSV(SE_SafetyPlace_has_Community,"SE_SafetyPlace_has_Community.csv")
SE_ConflictArea = nfv.dfFix(WomenGroup,"Trouble_Spots","Cooking_Details:INSTRUCTION_001")
SE_ConflictArea = SE_ConflictArea.dropna()
SE_ConflictArea = nt.separateValues(SE_ConflictArea)
nt.mkCSV(SE_ConflictArea,"SE_ConflictArea.csv") #IMPORRANTE Los datos entran como string de lugares, pero se quiere guardar coordenadas.
df1 = nfv.dfFix(LocalLeaders,"Settlement_security:secur_committees","Food_security:cultivation_months")
df1 = df1.isin(["yes"])
df2 = nfv.dfFix(Entities,"Women_Patrol","Education_Issues")
df2 = df2.isin(["Yes"])
SE_SafetyCommittee = nt.concatDF(df1,df2)
nt.mkCSV(SE_SafetyCommittee,"SE_SafetyCommittee.csv")
SE_SafetyLatrines = nfv.dfFix(SanitationInfra, "Public_Latrines:Sex_segregated","Slab")
SE_SafetyLatrines = SE_SafetyLatrines.isin(["yes"])
nt.mkCSV(SE_SafetyLatrines,"SE_SafetyLatrines.csv")
def NMsqlBody(communityType, tablesList, f, cursor, query):
otherTable = np.array([])
for row in tablesList:
for elem in row:
if ((communityType == 0 and elem.find("_has_camp") == -1)
or (communityType == 1)):
if (nmw.NMTable(elem)):
x = nmt.getTableName(elem)
if (nmt.is_non_zero_file(nfv.getPath(
finalpath, x + ".csv"))):
if (nmw.specialTable(elem) == False):
f.write(query.getQuery1() + elem + ".csv'\n" +
query.getQuery2() + " " + elem + "\n" +
query.getQuery3() + "\n" +
query.getQuery4() + "\n")
cursor.execute("SHOW columns FROM " + elem)
columnList = cursor.fetchall()
string = np.array([], dtype=str)
for column in columnList:
if (nmw.validColumn(column)):
string = np.append(string, column[0])
f.write(" (")
for column in string:
if (column != string[-1]):
f.write("@" + column + ",")
else:
f.write("@" + column + ")\n")
f.write("SET ")
for column in string:
if (column != string[-1]):
if (column == string[0]):
f.write(column + " = NULLIF(@" +
column + ",''),\n")
else:
f.write(" " + column +
" = NULLIF(@" + column +
",''),\n")
else:
if (column == string[0]):
f.write(column + " = NULLIF(@" +
column + ",'');\n\n")
else:
f.write(" " + column +
" = NULLIF(@" + column +
",'');\n\n")
else:
otherTable = np.append(otherTable, elem)
for elem in otherTable:
if (elem == "inf_expandplanbeneficiaries_has_inf_energyinfrastructure"
):
nmw.writteExpanPlan(query, f, cursor, communityType)
def knoweledge(GeneralCitizen):
S_Tecknowlege = ["Phone Call", "Internet", "PC", "Programming"]
S_Tecknowlege = pd.DataFrame(S_Tecknowlege)
nt.mkCSV(S_Tecknowlege, "S_Tecknowlege.csv")
S_Tecknowlege_has_Community = nfv.dfFix(GeneralCitizen,
"TICs_Knowledge:Phone_Call",
"App_USED")
S_Tecknowlege_has_Community = S_Tecknowlege_has_Community.transpose()
nt.mkCSV(S_Tecknowlege_has_Community, "S_Tecknowlege_has_Community.csv")
S_App = [
"whatsapp", "facebook", "skype", "instagram", "google", "youtube",
"email", "word", "excel", "otra"
]
S_App = pd.DataFrame(S_App)
nt.mkCSV(S_App, "S_App.csv")
df1 = nfv.dfFix(GeneralCitizen, "App_USED", "App_needed")
df1 = nt.set_sector(df1, "Used")
df2 = nfv.dfFix(GeneralCitizen, "App_needed", "Type_Food:Meat")
df2 = nt.set_sector(df2, "Necesity")
S_App_has_Community = nt.concatDF(df1.T, df2.T).T
nt.mkCSV(S_App_has_Community, "S_App_has_Community.csv")
def get_specialTableFKs(table, tableHas, x, y, cursor):
cursor.execute("SELECT * FROM " + table)
df1 = uniFormatTable(pd.DataFrame(cursor.fetchall()))
df2 = pd.read_csv(nfv.getPath(finalpath, tableHas + ".csv"),
header=None,
float_precision="high")
df2 = uniFormatDF(df2)
df1 = np.array(df1)
pk = np.array([])
arrayCommunity = np.array([])
communityPK = get_communityPK(tableHas, cursor)
for index, row in df2.iterrows():
for row2 in df1:
if (row[x] == row2[1]):
pk = np.append(pk, row2[0])
pk = pd.DataFrame(pk)
for index, row in pk.iterrows():
arrayCommunity = np.append(arrayCommunity, communityPK[0][0])
arrayCommunity = pd.DataFrame(arrayCommunity)
result = nt.concatDF(pk, nt.concatDF(arrayCommunity, df2))
result = result.drop(result.columns[[y]], axis=1)
return result
def infWater(Entities, HouseHold, WaterInf):
df1 = nfv.dfFix(Entities, "Water:Quality", "Water:Treatment")
df2 = nfv.dfFix(Entities, "Water:Comsuption", "Water:Time")
INF_WaterInfrastructure = nt.concatDF(df1, df2)
nt.mkCSV(INF_WaterInfrastructure, "INF_WaterInfrastructure.csv")
INF_TimeSpent = nfv.dfFix(HouseHold, "Water:Water_col",
"health_001:Healthcare")
nt.mkCSV(INF_TimeSpent, "INF_TimeSpent.csv")
INF_Purificationsystem = nfv.dfFix(Entities, "Water:Treatment",
"Water:Comsuption")
INF_Purificationsystem = nt.separateValues(INF_Purificationsystem)
nt.mkCSV(INF_Purificationsystem, "INF_Purificationsystem.csv")
df1 = nfv.dfFix(WaterInf, "Record_your_current_location:Latitude",
"Record_your_current_location:Accuracy")
df2 = nfv.dfFix(WaterInf, "Availability", "meta:instanceID")
df2 = df2.isin(["yes"])
INF_WaterPoint = nt.concatDF(df1, df2)
nt.mkCSV(INF_WaterPoint, "INF_WaterPoint.csv")
def energy(GeneralForm, Entities, Business, HouseHold, ComunalServices,
WomenGroup, EnergyINF):
df1 = nfv.dfFix(GeneralForm, "Energy:electrical_grid",
"Energy:power_point")
df1 = df1.isin(["yes"])
df2 = nfv.dfFix(Entities, "ENERGY:Electricity_network",
"ENERGY:Covered_services")
df2 = df2.isin(["yes"])
flag = False
for row in np.array(df2):
for elem in row:
if (flag == False and elem == True):
flag = True
df2 = pd.DataFrame(np.array([flag]))
df3 = nfv.dfFix(Entities, "ENERGY:Power_failure", "ENERGY:Street_Light")
x = np.array([])
x = np.append(x, df3['ENERGY:Power_failure'].dropna().mean())
flag = False
for row in np.array(df2):
for elem in row:
if (flag == False and elem == 'available'):
flag = True
if (flag):
y = pd.DataFrame(np.array(["available"]))
else:
y = pd.DataFrame(np.array(["not_available"]))
df3 = nt.concatDF(pd.DataFrame(x), y)
df4 = nfv.dfFix(Entities, "ENERGY:Street_Light",
"Urban_Planning_001:Urban_Planning")
df4 = df4.isin(["yes"])
flag = False
for row in np.array(df4):
for elem in row:
if (flag == False and elem == True):
flag = True
df4 = pd.DataFrame(np.array([flag]))
df5 = nfv.dfFix(GeneralForm, "Energy:Distance_ST",
"Transport:Kind_transport_inside")
INF_EnergyInfrastructure = nt.concatDF(
df1, (nt.concatDF(df2, nt.concatDF(df3, nt.concatDF(df4, df5)))))
nt.mkCSV(INF_EnergyInfrastructure, "INF_EnergyInfrastructure.csv")
inf_expandplanbeneficiaries = nfv.dfFix(Entities,
"ENERGY:Covered_services",
"ENERGY:Power_failure")
inf_expandplanbeneficiaries = nt.separateValues(
inf_expandplanbeneficiaries)
nt.mkCSV(inf_expandplanbeneficiaries, "inf_expandplanbeneficiaries.csv")
INF_GenerationSource = [
'electrical_gri', 'diesel_genset', 'solar_energy', 'other'
]
INF_GenerationSource = pd.DataFrame(INF_GenerationSource)
nt.mkCSV(INF_GenerationSource, "INF_GenerationSource.csv")
df1 = nfv.dfFix(Business, "Energy:access_by",
"Energy:electrical_appliances")
df2 = nfv.dfFix(Business, "Energy:money_electricity",
"Energy:cost_solar_panel")
comercial = nt.concatDF(df1, df2)
comercial = nt.set_sector(comercial, "comercial")
df1 = nfv.dfFix(HouseHold, "Energy:Access_electric", "Energy:Appliances")
df2 = nfv.dfFix(HouseHold, "Energy:Elec_expen", "Energy:Solar_cost")
residencial = nt.concatDF(df1, df2)
residencial = nt.set_sector(residencial, "residencial")
residencial = nt.replacestr(residencial, "electrical_gri_1",
"electrical_gri") #REVISAR OTRAS OPCIONES
comunitario = nfv.dfFix(ComunalServices, "Energy_Details:Energy_Source",
"Energy_Details:Type_of_water_supply")
comunitario = nt.set_sector(comunitario, "comunitario")
comunitario = nt.replacestr(comunitario, "thermal_genera", "diesel_genset")
INF_GenerationSource_has_Community = nt.concatDF(
comercial.T, nt.concatDF(residencial.T, comunitario.T)).T
nt.mkCSV(INF_GenerationSource_has_Community,
"INF_GenerationSource_has_Community.csv")
df1 = nfv.dfFix(EnergyINF, "Ofert:Type_of_water_supply", "Ofert:Picture")
df2 = nfv.dfFix(EnergyINF, "Ofert:Power_of_generation",
"Ofert:Power_of_generation_001")
INF_GenerationSystem = nt.concatDF(df1, df2)
nt.mkCSV(INF_GenerationSystem, "INF_GenerationSystem.csv")
INF_Appliance = np.array([
"lantern", "light_bulbs", "mobile_phone", "radio_music_pl", "tv_dvd",
"laptop_tablet_", "fridge", "electrical_sto", "others"
])
INF_Appliance = pd.DataFrame(INF_Appliance)
nt.mkCSV(INF_Appliance, "INF_Appliance.csv")
comercial = nfv.dfFix(Business, "Energy:electrical_appliances",
"Energy:money_electricity")
comercial = nt.dropNaAndResetIndex(comercial)
comercial = nt.get_applianceDF(comercial)
comercial = nt.set_sector(comercial, "comercial")
residencial = nfv.dfFix(HouseHold, "Energy:Appliances",
"Energy:Elec_expen")
residencial = nt.dropNaAndResetIndex(residencial)
residencial = nt.get_applianceDF(residencial)
residencial = nt.set_sector(residencial, "residencial")
comunitario = nfv.dfFix(ComunalServices,
"Energy_Details:Electrical_Appliances:Devices",
"Construction_Details:Appropiate_Roof")
comunitario = nt.dropNaAndResetIndex(comunitario)
comunitario = nt.get_applianceDF(comunitario)
comunitario = nt.set_sector(comunitario, "comunitario")
INF_Appliance_has_Community = nt.concatDF(
comercial.T, nt.concatDF(residencial.T, comunitario.T)).T
INF_Appliance_has_Community = INF_Appliance_has_Community[
INF_Appliance_has_Community[1].notna()]
nt.mkCSV(INF_Appliance_has_Community, "INF_Appliance_has_Community.csv")
df1 = nfv.dfFix(GeneralForm, "Energy:Stove", "Energy:Firewood_weight")
df2 = nfv.dfFix(GeneralForm, "Energy:fuel_cooking",
"Energy:technology_street_lighting")
df3 = nfv.dfFix(GeneralForm, "Energy:Firewood_weight",
"Energy:fuel_cooking")
INF_Kitchen = nt.concatDF(df1, nt.concatDF(df2, df3))
nt.mkCSV(INF_Kitchen, "INF_Kitchen.csv")
INF_CookWoman = nfv.dfFix(WomenGroup, "Cooking_Details:Cooking_Inside",
"Street_light")
nt.mkCSV(INF_CookWoman, "INF_CookWoman.csv")
df1 = nfv.dfFix(Entities, "ENERGY:Street_Light",
"Urban_Planning_001:Urban_Planning")
df1 = df1.isin(["yes"])
df2 = nfv.dfFix(GeneralForm, "Energy:Distance_ST",
"Transport:Kind_transport_inside")
df3 = nfv.dfFix(WomenGroup, "Feel_Safe:Street_Night",
"Feel_Safe:Bath_Area")
INF_PublicLighting = nt.concatDF(df1, (nt.concatDF(df2, df3)))
nt.mkCSV(INF_PublicLighting, "INF_PublicLighting.csv")
INF_LightingTech = nfv.dfFix(GeneralForm,
"Energy:technology_street_lighting",
"Energy:Distance_ST")
nt.mkCSV(INF_LightingTech, "INF_LightingTech.csv")
df1 = nfv.dfFix(EnergyINF, "Item", "Sector")
df1 = df1.isin(["street light"])
INF_StreetLamp = nfv.dfFix(EnergyINF,
"Record_your_current_location:Latitude",
"Record_your_current_location:Accuracy")
INF_StreetLamp = nt.get_valueBySector(df1, INF_StreetLamp)
nt.mkCSV(INF_StreetLamp, "INF_StreetLamp.csv")
def __init__(self, communityType):
Bibliography = pd.read_excel(
nfv.getPath(nfv.mainpath, "Bibliography_120220.xlsx"))
Bibliography = nfv.fixBibliography(Bibliography)
self.Bibliography = nfv.setDataByIndex(Bibliography, communityType)
self.Entities = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath, "NAUTIA_1_0_Entities_Interview_results.csv"),
float_precision="high"), communityType)
self.LocalLeaders = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(nfv.mainpath,
"NAUTIA_1_0_Local_leaders_v3_results.csv"),
float_precision="high"), communityType)
self.HouseHold = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath, "NAUTIA_1_0_Survey_household_v6_results.csv"),
float_precision="high"), communityType)
self.WomenGroup = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath, "NAUTIA_1_0_Women_Focus_Group2_results.csv"),
float_precision="high"), communityType)
self.SanitationInfra = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath,
"NAUTIA_V1_0_Sanitation_Infrastructre_results.csv"),
float_precision="high"), communityType)
self.Priorities = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(nfv.mainpath,
"NAUTIA_1_0_Priorities_v3_results.csv"),
float_precision="high"), communityType)
self.GeneralForm = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(nfv.mainpath,
"NAUTIA_1_0_General_form_v3_results.csv"),
float_precision="high"), communityType)
self.PublicSpace = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(nfv.mainpath,
"NAUTIA_1_0_Public_Space_results.csv"),
float_precision="high"), communityType)
self.WaterInf = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath, "NAUTIA_1_0_Water_Infrastructure_results.csv"),
float_precision="high"), communityType)
self.SanitationInf = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath,
"NAUTIA_V1_0_Sanitation_Infrastructre_results.csv"),
float_precision="high"), communityType)
self.WasteManagementInf = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath,
"NAUTIA_1_0_Waste_Management_Infrastructure_results.csv"),
float_precision="high"), communityType)
self.EnergyINF = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath, "NAUTIA_1_0_Energy_Infrastructure_results.csv"),
float_precision="high"), communityType)
self.Business = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath, "NAUTIA1_0_Business_surveys_v3_results.csv"),
float_precision="high"), communityType)
self.MobilityINF = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath,
"NAUTIA_1_0__Transport_servicesaccess_points_results.csv"),
float_precision="high"), communityType)
self.ComunalServices = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath, "NAUTIA_1_0_Communal_Services_results.csv"),
float_precision="high"), communityType)
self.GeneralCitizen = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath,
"NAUTIA_1_0_General_Citizen_Focus_Group_results.csv"),
float_precision="high"), communityType)
self.Shelter = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(nfv.mainpath,
"NAUTIA_1_0_Shelter_results.csv"),
float_precision="high"), communityType)
self.FarmyardCrop = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(
nfv.mainpath, "NAUTIA_1_0_Farmyard_and_Crops_results.csv"),
float_precision="high"), communityType)
def seEconomy(LocalLeaders,HouseHold,Priorities):
SE_Economy = nfv.dfFix(LocalLeaders, "Costs:cost_basic_basket","Costs:cost_firewood")
nt.mkCSV(SE_Economy,"SE_Economy.csv")
SE_Incometype = nfv.dfFix(HouseHold, "Economy:Main_inco","Economy:Money")
nt.mkCSV(SE_Incometype,"SE_Incometype.csv")
df1 = nfv.dfFix(HouseHold, "General:Gender","General:Settlement")
df2 = nfv.dfFix(HouseHold, "Economy:Money","Economy:Food")
SE_IncomeTtype_has_Community = nt.concatDF(df1,df2)
nt.mkCSV(SE_IncomeTtype_has_Community,"SE_IncomeTtype_has_Community.csv")
SE_ExpenseType = ['food','clothes','water','education','transport','health','energy']
SE_ExpenseType = pd.DataFrame(SE_ExpenseType)
nt.mkCSV(SE_ExpenseType,"SE_ExpenseType.csv")
df1 = nfv.dfFix(HouseHold, "General:Gender","General:Settlement")
df2 = nfv.dfFix(HouseHold, "Economy:Food","meta:instanceID")
df3 = pd.DataFrame(['food','clothes','water','education','transport','health','energy'])
SE_ExpenseType_has_Community = nt.get_claveValor(df1,df2,df3)
nt.mkCSV(SE_ExpenseType_has_Community,"SE_ExpenseType_has_Community.csv")
priority = ['energy','shelter','water access','sanitation','education','health','public space','food','TIC','work','waste management','public transport','religious center','socio cultural center','market']
priority = pd.DataFrame(priority)
nt.mkCSV(priority,"se_priority.csv")
df1 = nfv.dfFix(Priorities,"group_yf0yl72:Energy_1","Priority_2:Instruction")
df2 = nfv.dfFix(Priorities,"Priority_2:Energy_2","Priority_3:Instruction_001")
df3 = nfv.dfFix(Priorities,"Priority_3:Energy_3","Priority_4:Instruction_002")
df4 = nfv.dfFix(Priorities,"Priority_4:Energy_4_001","Priority_5:Instruction_003")
df5 = nfv.dfFix(Priorities,"Priority_5:Energy_4","meta:instanceID")
SE_Priority_has_Community = nt.concatDF(df1,(nt.concatDF(df2,nt.concatDF(df3,nt.concatDF(df4,df5)))))
SE_Priority_has_Community = pd.DataFrame(SE_Priority_has_Community.sum())
priority = np.array(['energy','shelter','water access','sanitation','education','health','public space','food','TIC','work','waste management','public transport','religious center','socio cultural center','market'])
array1 = np.array([])
array2 = np.array([])
for i in range(5):
for elem in priority:
array1 = np.append(array1,elem)
array2 = np.append(array2,i+1)
SE_Priority_has_Community = nt.concatDF(SE_Priority_has_Community,nt.concatDF(pd.DataFrame(array1),pd.DataFrame(array2)))
nt.mkCSV(SE_Priority_has_Community,"SE_Priority_has_Community.csv")
def urbanism(Entities,GeneralForm,PublicSpace):
df1 = nfv.dfFix(Entities,"Urban_Planning_001:Urban_Planning","Urban_Planning_001:Growth_area")
df2 = nfv.dfFix(GeneralForm,"Urban_information:Boundary_limits","Urban_information:Drain_system")
df3 = nfv.dfFix(Entities,"Urban_Planning_001:Land_Managment","Urban_Planning_001:Risk_Managment")
df4 = nfv.dfFix(Entities,"Urban_Planning_001:Growth_area","Urban_Planning_001:Land_Managment")
df5 = nfv.dfFix(Entities,"Urban_Planning_001:Risk_Managment","Shelter:Housing_Improvement")
U_Urbanism = nt.concatDF(df1,nt.concatDF(df2,nt.concatDF(df3,nt.concatDF(df4,df5))))
nt.mkCSV(U_Urbanism,"U_Urbanism.csv")
u_area = nfv.dfFix(PublicSpace,"Details:Shade_Areas","meta:instanceID")
u_area = nt.getSubColumnNames(u_area,8)
u_area = pd.DataFrame(u_area)
nt.mkCSV(u_area,"u_area.csv")
df1 = nfv.dfFix(PublicSpace,"Details:Shade_Areas","meta:instanceID")
df1 = df1.isin(["yes"])
u_area = nt.getSubColumnNames(df1,8)
df = pd.DataFrame()
U_Area_has_Community = nfv.dfFix(PublicSpace,"Record_your_current_location:Latitude","Record_your_current_location:Accuracy")
j = 0
for row in np.array(df1):
i = 0
array = np.array([],dtype = int)
array2 = np.array([])
for elem in row:
if(elem == True):
array = np.append(array,i)
i += 1
for i in array:
array2 = np.append(array2,np.array(u_area)[i])
df = nt.concatDF(df,pd.DataFrame(array2))
j += 1
U_Area_has_Community = nt.concatDF(U_Area_has_Community,df.T)
df = pd.DataFrame()
for row in np.array(U_Area_has_Community):
array = np.array([])
for elem in row[3:]:
if(str(elem) != 'nan'):
array = np.append(array,elem)
for elem in array:
array2 = row[:3]
array2 = np.append(array2,elem)
df = nt.concatDF(df,pd.DataFrame(array2))
U_Area_has_Community = df.T
nt.mkCSV(U_Area_has_Community,"U_Area_has_Community.csv")
#U_LandUse Datos GIS, no parte de esta ETL. 2 no se encuentran datos.
U_Road = nfv.dfFix(GeneralForm,"Urban_information:Drain_system","Energy:electrical_grid")
nt.mkCSV(U_Road,"U_Road.csv") #Falta la información que sale de Plano
df1 = nfv.dfFix(PublicSpace,"Record_your_current_location:Latitude","Record_your_current_location:Accuracy")
df2 = nfv.dfFix(PublicSpace,"Details:Shade_Areas","meta:instanceID")
df2 = df2.isin(["yes"])
U_RecreationalArea = nt.concatDF(df1,df2)
nt.mkCSV(U_RecreationalArea,"U_RecreationalArea.csv")
#U_PublicSpace dato , no corresponde a la ETL
def generalData(Bibliography):
df1 = nfv.dfFix(Bibliography, "Mujeres menores de 5 años (%)",
"Total population")
df2 = nfv.dfFix(Bibliography, "Growth rate of populatoin (%)", "Culture")
GD_Demography = nt.concatDF(df1, df2)
nt.mkCSV(GD_Demography, "GD_Demography.csv")
GD_Ethnicgroup = nfv.dfFix(Bibliography, "Ethnich group 1", "Religion").T
nt.mkCSV(GD_Ethnicgroup, "GD_Ethnicgroup.csv")
df1 = nfv.dfFix(Bibliography, "Parliamentary republic",
"Territorial and Urbanistic")
GD_Government = df1
GD_Government = GD_Government.isin(["Si"])
GD_Government = GD_Government.any(
) #Lista con indice de columna y True si un contiene un True o False en caso contrario
GD_Government = list(
GD_Government[GD_Government == True].index) #lista de indices con true
GD_Government = pd.DataFrame(GD_Government)
nt.mkCSV(GD_Government, "GD_Government.csv")
GD_Economy = nfv.dfFix(Bibliography, "Agriculture (%)", "Government")
nt.mkCSV(GD_Economy, "GD_Economy.csv")
df1 = nfv.dfFix(Bibliography, "Urban population (%)", "Population density")
df2 = nfv.dfFix(Bibliography, "Urban (inhabitants/hectares)",
"Infrastructures")
GD_Urbanism = nt.concatDF(df1, df2)
nt.mkCSV(GD_Urbanism, "GD_Urbanism.csv")
df1 = nfv.dfFix(Bibliography, "Rural agua (%)",
"Access to improved sanitation")
df2 = nfv.dfFix(Bibliography, "Rural saneamiento(%)",
"Access to electricity")
df3 = nfv.dfFix(Bibliography, "Rural electricidad (%)",
"Matrix of electricity generation")
GD_Infrastructure = nt.concatDF(nt.concatDF(df1, df2), df3)
nt.mkCSV(GD_Infrastructure, "GD_Infrastructure.csv")
GD_ElectricGenerationMix = nfv.dfFix(Bibliography, "Hydropower (%)",
"High voltage (kV)")
nt.mkCSV(GD_ElectricGenerationMix, "GD_ElectricGenerationMix.csv")
GD_ServiceAccess = nfv.dfFix(Bibliography, "Illiteracy rate (%)",
"Shelter")
nt.mkCSV(GD_ServiceAccess, "GD_ServiceAccess.csv")
GD_Shelter = nfv.dfFix(Bibliography, "Slum population rate (%)",
"SPECIFIC INFORMATION - SETTLEMENTS LEVEL")
nt.mkCSV(GD_Shelter, "GD_Shelter.csv")
Comun = pd.read_excel(nfv.getPath(nt.mainpath, "Bibliography_120220.xlsx"))
Comun = nfv.fixBibliography(Comun)
GD_Religion = nfv.dfFix(Comun, "Religion 1", "Language")
df1 = nfv.dropRow(GD_Religion, 1)
np_array1 = np.array(df1)
df2 = nfv.dropRow(GD_Religion, 0)
np_array2 = np.array(df2)
np_array3 = np.concatenate((np_array1, np_array2), axis=1)
GD_Religion = pd.DataFrame(np_array3)
GD_Religion = GD_Religion.transpose()
GD_Religion = GD_Religion[0].unique()
GD_Religion = pd.DataFrame(GD_Religion)
GD_Religion = GD_Religion.dropna()
nt.mkCSV(GD_Religion, "GD_Religion.csv")
GD_Language = nfv.dfFix(Comun, "Language 1", "Economy and well-being")
df1 = nfv.dropRow(GD_Language, 1)
np_array1 = np.array(df1)
df2 = nfv.dropRow(GD_Language, 0)
np_array2 = np.array(df2)
np_array3 = np.concatenate((np_array1, np_array2), axis=1)
GD_Language = pd.DataFrame(np_array3)
GD_Language = GD_Language.transpose()
GD_Language = GD_Language[0].unique()
GD_Language = pd.DataFrame(GD_Language)
GD_Language = GD_Language.dropna()
nt.mkCSV(GD_Language, "GD_Language.csv")
def campData(Bibliography, Entities, LocalLeaders):
Camp_MovementReason = nfv.dfFix(Bibliography, "Reason 1", "Climate")
Camp_MovementReason = Camp_MovementReason.transpose()
nt.mkCSV(Camp_MovementReason, "Camp_MovementReason.csv")
Camp_Integration = nfv.dfFix(Entities, "GENERAL_INFORMATION:Relationship",
"GENERAL_INFORMATION:Movement_outside")
nt.mkCSV(Camp_Integration, "Camp_Integration.csv")
Camp_NaturalHazard = nfv.dfFix(Entities,
"Enviormental_Issues:Risk:Risk_Flood",
"Enviormental_Issues:Deforestation")
Camp_NaturalHazard = nt.getSubColumnNames(Camp_NaturalHazard, 30)
nt.mkCSV(Camp_NaturalHazard, "Camp_NaturalHazard.csv")
Camp_NaturalHazard_Has_Camp = nfv.dfFix(
Entities, "Enviormental_Issues:Risk:Risk_Flood",
"Enviormental_Issues:Deforestation")
hazards = nt.getSubColumnNames(Camp_NaturalHazard_Has_Camp, 30)
Camp_NaturalHazard_Has_Camp = nt.get_number(Camp_NaturalHazard_Has_Camp,
hazards)
nt.mkCSV(Camp_NaturalHazard_Has_Camp, "Camp_NaturalHazard_Has_Camp.csv")
Camp_LocalVegetation = nfv.dfFix(Entities,
"Enviormental_Issues:Native_Plant",
"Enviormental_Issues:Native_Crops")
Camp_LocalVegetation = nt.separateValues(Camp_LocalVegetation)
nt.mkCSV(Camp_LocalVegetation,
"Camp_LocalVegetation.csv") # ¿MODIFICAR FOLMULARIO?
Camp_LocalCrop = nfv.dfFix(Entities, "Enviormental_Issues:Native_Crops",
"Water_table")
Camp_LocalCrop = nt.separateValues(Camp_LocalCrop)
nt.mkCSV(Camp_LocalCrop, "Camp_LocalCrop.csv") #MODIFICAR FOLMULARIO?
df3 = nfv.dfFix(Entities, "Enviormental_Issues:High_enviormental_value",
"Enviormental_Issues:Native_Plant")
df3 = df3.isin(["yes"])
df4 = nfv.dfFix(Entities, "Enviormental_Issues:Deforestation",
"Enviormental_Issues:High_enviormental_value")
Camp_Enviroment = nt.concatDF(df3, df4)
nt.mkCSV(Camp_Enviroment, "Camp_Enviroment.csv")
df1 = nfv.dfFix(Bibliography, "Tropical (Write one: Af, Aw or Am)",
"Temperature")
df1 = df1.transpose()
df1 = df1.dropna()
df1 = df1.transpose()
df2 = nfv.dfFix(Bibliography, "Max (ºC)", "Annual precipitation")
df3 = nfv.dfFix(Bibliography, "Max (mm)", "Additional information")
Camp_ClimaticRegion = nt.concatDF(df1, nt.concatDF(df2, df3))
nt.mkCSV(Camp_ClimaticRegion, "Camp_ClimaticRegion.csv")
Camp_EnergySource = [
'diesel', 'Kerosene', 'Ethanol', 'gas', 'firewood', 'diesel genset',
'electricity', 'solar panel'
]
Camp_EnergySource = pd.DataFrame(Camp_EnergySource)
nt.mkCSV(Camp_EnergySource, "Camp_EnergySource.csv")
df1 = nfv.dfFix(Entities, "Fuel_Cost:Fuel_Cost_Diesel",
"ENERGY:Electricity_network")
df2 = nfv.dfFix(LocalLeaders, "Costs:cost_firewood", "meta:instanceID")
Camp_EnergySource_Has_Camp = nt.concatDF(df1, df2)
source = pd.DataFrame([
'diesel', 'Kerosene', 'Ethanol', 'gas', 'firewood', 'diesel genset',
'electricity', 'solar panel'
])
Camp_EnergySource_Has_Camp = nt.get_number(Camp_EnergySource_Has_Camp,
source)
nt.mkCSV(Camp_EnergySource_Has_Camp, "Camp_EnergySource_Has_Camp.csv")
Camp_Mobility = nfv.dfFix(Entities, "GENERAL_INFORMATION:Movement_outside",
"Population:Women:Infants")
nt.mkCSV(Camp_Mobility, "Camp_Mobility.csv")
Camp_Shelter = nfv.dfFix(Entities, "Shelter:Housing_Improvement",
"Shelter:Total_shelter")
nt.mkCSV(Camp_Shelter, "Camp_Shelter.csv")
def writteExpanPlan(query, f, cursor, communityType):
Entities = nfv.setDataByIndex(
pd.read_csv(nfv.getPath(mainpath,
"NAUTIA_1_0_Entities_Interview_results.csv"),
float_precision="high"), communityType)
inf_expandplanbeneficiaries = nfv.dfFix(Entities,
"ENERGY:Covered_services",
"ENERGY:Power_failure")
inf_expandplanbeneficiaries = nt.separateValues(
inf_expandplanbeneficiaries)
cursor.execute("SELECT * FROM inf_expandplanbeneficiaries")
array = np.array(cursor.fetchall())
pk = np.array([])
for row in array:
for row2 in np.array(inf_expandplanbeneficiaries):
for elem in row2:
if (row[1] == elem):
pk = np.append(pk, elem)
cursor.execute(
"SELECT idINF_EnergyInfrastructure FROM INF_EnergyInfrastructure ORDER BY idINF_EnergyInfrastructure DESC LIMIT 1"
)
pkInf = np.array(cursor.fetchall())
cursor.execute(
"SELECT Community_idCommunity FROM INF_EnergyInfrastructure ORDER BY Community_idCommunity DESC LIMIT 1"
)
fkInf = np.array(cursor.fetchall())
v1 = v2 = np.array([])
for elem in pk:
v1 = np.append(v1, pkInf)
v2 = np.append(v2, fkInf)
result = nt.concatDF(pd.DataFrame(pk),
nt.concatDF(pd.DataFrame(v1), pd.DataFrame(v2)))
nt.mkCSV(result,
"inf_expandplanbeneficiaries_has_inf_energyinfrastructure.csv")
cursor.execute(
"SHOW columns FROM inf_expandplanbeneficiaries_has_inf_energyinfrastructure"
)
columnList = cursor.fetchall()
table = "inf_expandplanbeneficiaries_has_inf_energyinfrastructure"
f.write(query.getquery1() + table + ".csv'\n" + query.getquery2() + " " +
table + "\n" + query.getquery3() + "\n" + query.getquery4() + "\n")
pk = True
string = np.array([], dtype=str)
for column in columnList:
if (pk):
pk = False
else:
string = np.append(string, column[0])
f.write(" (")
for column in string:
if (column != string[-1]):
f.write("@" + column + ",")
else:
f.write("@" + column + ")\n")
f.write("SET ")
for column in string:
if (column != string[-1]):
if (column == string[0]):
f.write(column + " = NULLIF(@" + column + ",''),\n")
else:
f.write(" " + column + " = NULLIF(@" + column + ",''),\n")
else:
if (column == string[0]):
f.write(column + " = NULLIF(@" + column + ",'');\n\n")
else:
f.write(" " + column + " = NULLIF(@" + column + ",'');\n\n")